LATIDIA · Investigación

Aprendizaje de refuerzo multiagente mejorado por LLM para vehículos eléctricos unificados: optimización de la estación de carga y la red en sistemas de carga pública

arXiv: 2609.13805v1Tipo de anuncio: nuevo Resumen: En la era del Internet de las cosas (IoT), coordinar la programación de carga de vehículos eléctricos (EV) conectados para equilibrar la satisfacción de carga de EV, la rentabilidad de la estación y

La noticia

arXiv:2609.13805v1 Announce Type: new Abstract: In the era of the Internet of Things (IoT), coordinating connected electric vehicle (EV) charging scheduling to balance EV charging satisfaction, station profitability, and smart grid stability presents a complex multi-objective challenge. Existing Multi-Agent Reinforcement Learning (MARL) approaches often struggle with high-dimensional state spaces generated by massive IoT sensing data and conflicting stakeholder interests. This paper proposes a novel LLM-enhanced MARL framework that, for the first time, simultaneously optimizes the Grid, EVs, and Stations within a unified loop. By integrating Large Language Model (LLM), we address two critical bottlenecks: interpretable feature selection and adaptive multi-objective balancing. The LLM analyzes real-time IoT-collected environmental states to extract